Engineering

Solution Engineer At Digital Divide Data

Digital Divide Data·Nairobi, kenya·Full Time·Contract
EngineeringFull TimeContract
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What you'll do

  • Design and build robust data ingestion pipelines, transforming raw CSV/TSV data into structured, queryable datasets.
  • Model custom entities and map diverse data sources into standardised target schemas.
  • Build and manage data environments using SQLite, Google Cloud SQL and BigQuery.
  • Develop and maintain Dockerised services for data loading, embedding generation and data-serving applications.
  • Deploy and operate production workloads on Google Cloud Run, with a focus on scalability, reliability and high availability.
  • Work across Redis, VPC networking, Artifact Registry and Cloud Storage.
  • Implement secure cloud architectures using Cloud IAM, VPC and Cloud Run ingress controls.
  • Automate data workflows and deployments through GitHub Actions, including scheduled ingestion, ID mapping, embedding generation and container builds.
  • Build and integrate AI-powered capabilities, including natural-language querying, semantic search, embeddings and LLM-powered workflows.
  • Contribute to emerging agentic architectures and integrations, including technologies such as Model Context Protocol (MCP).

What we're looking for

You bring:

  • 2+ years' experience in data engineering, cloud engineering or backend development.
  • Strong Python development skills, including practical experience with Pandas.
  • Solid experience with Docker and containerised applications.
  • Strong working knowledge of Google Cloud Platform, particularly cloud compute, managed databases, data warehousing, networking and IAM.
  • Strong SQL skills and experience working with both relational and analytical data stores.
  • Experience designing, building and consuming REST APIs.
  • A strong engineering mindset, with the ability to work independently, troubleshoot complex problems and take ownership from development through to production.

It's a plus if you have

  • Experience with AI/ML engineering, particularly embeddings, Sentence Transformers or LLM applications.
  • Exposure to agentic frameworks, MCP or other emerging AI integration patterns.
  • Experience with Flask and Jinja.
  • Familiarity with WSL and Google Cloud Shell.

Why this opportunity?

  • Because you'll be building more than pipelines.
  • You'll help create the technical foundation for intelligent, data-driven applications, where secure cloud infrastructure, high-quality data and AI come together.
  • If you're looking to deepen your expertise in cloud and data engineering while growing into applied AI, this role offers the opportunity to work on that evolution firsthand.

Key skills

BA/BSc/HNDDiploma
Apply Now

At a glance

Company

Digital Divide Data

Location

Nairobi, kenya

Employment

Full Time

Experience

Valid until

Not specified

Created

September 14, 2026

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Support data residency and sovereignty controls across Absa's operating countries. Work with architecture, security, risk, and legal stakeholders to ensure AI workloads comply with applicable data localisation and cross-border transfer requirements. Contribute to AI-specific threat modelling covering model endpoints, agentic workflows, third-party AI providers, model supply chains, APIs, vector stores, and adversarial machine learning risks. Remediate identified security vulnerabilities and configuration risks within agreed timelines. Maintain platform documentation and evidence required for security reviews, audits, architecture approvals, and risk governance processes. Apply Absa's Enterprise-Wide Risk Management Framework, Group Architecture standards, information security requirements, and AI Responsible Use Policy in all engineering activities. Agentic AI Infrastructure Support the deployment and operation of agent orchestration technologies such as LangGraph, Microsoft Azure AI Foundry Agent Service, Amazon Bedrock Agents, AutoGen, or equivalent frameworks. Configure infrastructure for agent tools, APIs, memory services, vector stores, workflow engines, and enterprise system integrations. Implement secure tool-calling patterns, including identity propagation, permission controls, audit logging, timeout management, and failure handling. Support agent state management, session persistence, memory controls, and multi-agent communication patterns. Implement monitoring and tracing for agent execution paths, tool calls, latency, errors, and resource consumption. Work with AI Solution Engineers to move agentic AI solutions from development into controlled, production-ready environments. Contribute to platform standards for agent testing, deployment, monitoring, rollback, and lifecycle management. Investigate and resolve infrastructure issues affecting the performance, security, or reliability of agentic AI workloads. Agile Delivery and Capability Development Participate actively in sprint planning, backlog refinement, daily stand-ups, technical demonstrations, and retrospectives. Estimate engineering effort and deliver assigned platform features within agreed timelines and quality standards. Collaborate with platform engineers, cloud engineers, AI Solution Engineers, architects, security specialists, data engineers, and business unit technology teams. Participate in code reviews, infrastructure reviews, testing, troubleshooting, and technical problem-solving. Contribute to platform engineering standards, reusable templates, automation libraries, and delivery accelerators. Maintain comprehensive technical documentation, architectural decision records, deployment guides, and operational runbooks. Share technical knowledge and provide guidance to junior engineers and other members of the engineering community. Support the development of platform onboarding materials, self-service documentation, and user guides for business unit technology teams. Proactively identify technical dependencies, delivery risks, and operational barriers and escalate these appropriately. Remain current with developments in cloud AI platforms, agentic AI, MLOps, FinOps, AI security, and platform engineering. Qualifications And Experience Education and Qualifications Bachelor's degree in Computer Science, Information Technology, Data Science, Mathematics, Statistics, Engineering, or a related quantitative discipline is essential. A postgraduate qualification is advantageous. Relevant practical experience may be considered where supported by a strong record of cloud and platform engineering delivery. Advantageous Certifications One or more of the following certifications would be advantageous: Cloud AWS Certified Solutions Architect AWS Certified Machine Learning Engineer Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Azure Solutions Architect Expert Databricks Certified Data Engineer or Machine Learning certification Infrastructure-as-Code HashiCorp Certified: Terraform Associate Equivalent Terraform, Pulumi, or cloud infrastructure certification FinOps FinOps Certified Practitioner Equivalent cloud cost management or financial operations certification Security Certified Cloud Security Professional AWS Certified Security Microsoft Security, Compliance, and Identity certification Equivalent cloud or cybersecurity certification Work Experience Approximately 4 to 6 years of relevant experience in cloud engineering, platform engineering, DevOps, MLOps, infrastructure engineering, or AI platform engineering. At least 2 years of practical experience supporting cloud-based data, machine learning, generative AI, or AI platform workloads in a production environment. Production experience with at least two of the following: AWS Bedrock or Amazon SageMaker Databricks Microsoft Azure AI Foundry or Azure Machine Learning Hugging Face Kubernetes-based model serving Practical infrastructure-as-code experience using Terraform, Pulumi, AWS CDK, or an equivalent technology. Experience building or supporting CI/CD pipelines for cloud infrastructure, platform components, data services, or machine learning workloads. Experience with Docker, Kubernetes, Helm, APIs, identity integration, and cloud-native platform services. Experience implementing monitoring, dashboards, alerts, and operational support processes for production platforms. Working knowledge of cloud cost management, cost allocation, capacity monitoring, and infrastructure optimisation. Experience applying cloud security controls, identity and access management, secrets management, and secure API integration. Experience working within enterprise risk, architecture, security, and change management processes. Experience in financial services, telecommunications, healthcare, insurance, or another regulated industry is advantageous. Knowledge And Skills Multi-Cloud AI Platform Engineering - Practical knowledge of designing, deploying, and supporting AI services across AWS, Microsoft Azure, Databricks, Hugging Face, or Kubernetes-based environments. Agentic AI Infrastructure - Working knowledge of agent orchestration frameworks, tool-calling API patterns, agent memory, state management, tracing, and multi-agent workflows. AI FinOps and Cost Management - Knowledge of cloud consumption models, token-based pricing, Databricks DBUs, provisioned throughput, GPU utilisation, chargeback and showback reporting, and spend anomaly detection. AI Security and Zero Trust - Working knowledge of OAuth 2.0, OIDC, JWT, RBAC, ABAC, API security, managed identities, secrets management, prompt injection controls, data loss prevention, and secure agent tool access. Infrastructure-as-Code - Strong practical experience with Terraform, Pulumi, AWS CDK, or equivalent infrastructure automation technologies. Containerisation and Orchestration - Experience with Docker, Kubernetes, Helm, container registries, workload scheduling, resource allocation, and production container operations. Platform Observability - Experience with Prometheus, Grafana, Datadog, OpenTelemetry, cloud-native monitoring tools, or Databricks Lakehouse Monitoring. Cloud-Agnostic Model Serving - Working knowledge of containerised model deployment and serving technologies such as ONNX, BentoML, Triton Inference Server, Kubernetes, or equivalent frameworks. MLOps Tooling - Working knowledge of MLflow, Kubeflow, Airflow, model registries, feature stores, automated testing, and CI/CD for machine learning workloads. GPU Infrastructure - Understanding of GPU workload deployment, capacity management, right-sizing, spot instance strategies, and cost optimisation for model training and inference. Enterprise Risk and Governance - Working knowledge of information security, technology risk, architecture governance, responsible AI, privacy, data residency, and change management requirements within a regulated environment. Agile Delivery - Experience working in agile engineering teams using sprint planning, backlog management, iterative delivery, peer review, testing, and continuous improvement practices. Education Bachelor's Degree: Information Technology

a month ago

Solution Area Specialists Manager Ai Business At Microsoft

Microsoft

Nairobi, kenya

full-time

Overview As the Solution Sales Manager - AI Business Solutions, you will lead the strategy, execution, and people leadership required to accelerate customer transformation through Microsoft Business Applications, AI, and cloud solutions. This role provides direction and guidance across cross-functional teams, leading strategic engagements with executive stakeholders and decision makers for high-value customers to deepen relationships, identify business priorities, qualify opportunities, and accelerate revenue growth. You will define the overarching sales strategy for regional teams, including whitespace analysis, territory planning, and market opportunity identification. Leveraging AI-driven market intelligence, predictive analytics, and customer insights, you will proactively uncover business opportunities, assess emerging trends, identify market gaps, and guide sales initiatives that address complex customer priorities and measurable business outcomes. The role strategically directs teams across regions in the innovation, positioning, and deployment of AI- and cloud-enabled business solutions that help customers modernize operations, improve decision-making, strengthen customer engagement, and achieve sustainable transformation. You will empower specialists and solution engineers to deliver compelling solution value propositions, build trusted executive relationships, and demonstrate Microsoft's leadership in secure, responsible, AI-powered business transformation. In partnership with internal executive stakeholders, you will drive comprehensive pipeline reviews across the territory to improve forecasting accuracy, strengthen sales discipline, and exceed revenue targets. You will also oversee the development and implementation of customer-specific sales strategies aligned to each customer's security priorities, business application needs, and customer success planusing AI-powered analytics and insights to strengthen long-term partnerships and accelerate growth. As a people manager, you will lead and develop a high-performing team of Solution Area Specialists and Solution Engineers focused on Business Applications. This includes driving employee growth and development, coaching for impact, managing performance, executing strategic projects, and fostering a culture of collaboration, accountability, innovation, and customer obsession. In this role, you will also accelerate your career growth, develop deep business acumen, hone your selling skills, and become adept at deal structuring. Ultimately, this role is accountable for helping customers leverage Microsoft technology to achieve successful business outcomes while growing Microsoft revenue across the assigned territory. This opportunity supports flexible work arrangements, including the ability to work from home where business needs, customer commitments, and local policy allow. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Responsibilities Lead strategic customer engagement: Guide cross-functional teams in executive-level conversations with high-value customers to understand business priorities, shape transformation agendas, deepen relationships, and identify, qualify, and accelerate strategic sales opportunities. Define and execute regional sales strategy: Establish the overarching strategy for assigned markets, including whitespace analysis, account prioritization, opportunity development, territory planning, and competitive positioning for Microsoft Business Applications, AI, and cloud solutions. Uncover market opportunities and drive predictive planning: Leverage AI-driven market intelligence, trend analysis, customer insights, competitive signals, propensity data, renewal data, consumption data, and usage analytics to identify emerging needs, market gaps, and future demand across the territory. Accelerate AI and cloud transformation: Direct teams across regions in the innovation, positioning, and deployment of AI- and cloud-enabled solutions that advance customer transformation, integrate Microsoft Business Applications and AI capabilities, and deliver measurable business outcomes. Own pipeline discipline, forecast accuracy, and deal execution: Empower teams to conduct comprehensive sales pipeline reviews with internal executive stakeholders, strengthen pipeline hygiene, improve forecasting precision, support complex deal structuring, mitigate risks, and consistently exceed sales and consumption targets through AI-powered analytics and forecasting tools. Embed security, trust, customer success, and partner scale: Oversee customer-specific sales strategies aligned to security priorities and customer success plans, demonstrate Microsoft's commitment to secure and responsible AI-powered transformation, and activate partner ecosystems to drive co-selling, partner attach, adoption, value realization, and long-term customer partnerships. Lead, coach, and develop high-performing teams: Attract, develop, and retain strong talent; model Microsoft's culture and values; coach employees for growth; manage performance; build an inclusive and accountable team culture; and enable Specialists and Solution Engineers to operate as trusted advisors who translate customer priorities into business application solutions that drive Microsoft revenue growth. Qualifications Required Qualifications (RQs) 9+ years technology-related sales or account management experience OR Bachelor's Degree in Computer Science, Information Technology, Business Administration, or related field AND 8+ years of technology-related sales or account management experience. Preferred Qualifications (PQs) 12+ years technology-related sales or account management experience OR Bachelor's Degree in Information Technology, or related field AND 10+ years technology-related sales or account management experience OR Master's Degree in Business Administration (i.e., MBA), Information Technology, or related field AND 8+ years technology-related sales or account management experience. 10+ years services sales or account management experience. 5+ years people management experience. 2+ years manager of managers experience. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

2 months ago